Environmental changes in biodiversity hotspot ecosystems of
South Ecuador: RESPonse and feedback effECTs (FOR2730)

Abstract:

Seasonally dry forests in the neotropics are heavily threatened by a combination of human disturbances and climate change; however, the severity of these threats is seldom contrasted. This study aims to quantify and compare the effects of deforestation and climate change on the natural spatial ranges of 17 characteristic tree species of southern Ecuador dry deciduous forests, which are heavily fragmented and support high levels of endemism as part of the Tumbesian ecoregion. We used 660 plant records to generate species distribution models and land-cover data to project species ranges for two time frames: a simulated deforestation scenario from 2008 to 2014 with native forest to anthropogenic land-use conversion, and an extreme climate change scenario (CCSM4.0, RCP 8.5) for 2050, which assumed zero change from human activities. To assess both potential threats, we compared the estimated annual rates of species loss (i.e., range shifts) affecting each species. Deforestation loss for all species averaged approximately 71 km2/year, while potential climate-attributed loss was almost 21 km2/year. Moreover, annual area loss rates due to deforestation were significantly higher than those attributed to climate-change (P < 0.01). However, projections into the future scenario show evidence of diverging displacement patterns, indicating the potential formation of novel ecosystems, which is consistent with other species assemblage predictions as result of climate change. Furthermore, we provide recommendations for management and conservation, prioritizing the most threatened species such as Albizia multiflora, Ceiba trichistandra, and Cochlospermum vitifolium.

Abstract:

Ecosystems worldwide are threatened by the increasing impact of land use and climate change. To protect their diversity and functionality, spatially explicit monitoring systems are needed. In remote areas, monitoring is difficult and recurrent field surveys are costly. By using Lidar or themore cost-effective and repetitive optical satellite data, remote sensing could provide proxies for habitat structure supporting measures for the conservation of biodiversity. Here we compared the explanatory power of both, airborne Lidar and optical satellite data in modeling the spatial distribution of biodiversity of birds across a complex tropical mountain forest ecosystem in southeastern Ecuador. Weused data fromfield surveys of birds and chose three measures as proxies for different aspects of diversity: (i) Shannon diversity as a measure of ?-diversity that also includes the relative abundance of species, (ii) phylodiversity as a first proxy for functional diversity, and (iii) community composition as a proxy for combined ?- and ?-diversity.We modeled these diversity estimates using partial least-square regression of Lidar and optical texturemetrics separately and compared themodels using a leave-one-out validated R2 and rootmean square error. Bird community informationwas best predicted by both remote sensing datasets, followed by Shannon diversity and phylodiversity. Our findings reveal a high potential of optical texture metrics for predicting Shannon diversity and ameasure of community composition, but not for modeling phylodiversity. Generalizing from the investigated tropical mountain ecosystem, we conclude that texture information retrieved frommultispectral data of operational satellite systems could replace costly airborne laser-scanning formodeling certain aspects of biodiversity.

Abstract:

Future increase of atmospheric nitrogen deposition in tropical regions is expected to have negative impacts on forests ecosystems and related biogeochemical processes. In tropical mountain forests topography causes complex streamflow and rainfall patterns, governing the atmospheric transport of pollutants and the intensity and spatial variability of deposition. The main goal of the current study is to link spatio-temporal patterns of upwind nitrogen emissions and nitrate deposition in the San Francisco Valley (eastern Andes of southern Ecuador) at different altitudinal levels. The work is based on Scanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) retrieved-NO2 concentrations, NOx biomass burning emissions from the Global Fire Emissions Database (GFEDv3), and regional vehicle emissions inventory (SA-INV) for urban emissions in South America. The emission data is used as input for lagrangian atmospheric backward trajectory modeling (FLEXTRA) to model the transport to the study area. The results show that NO3 À concentrations in occult precipitation samples are significantly correlated to long-range atmospheric secondary nitrogen transport at the highest meteorological stations (MSs) only, whereas for NO3 À concentrations in rain samples this correlation is more pronounced at the lower MSs. We conclude that ion concentrations in occult precipitation at the uppermost MSs are mainly linked to distant emission sources via the synoptic circulation impinging the more exposed higher sites. Lower correlations close to the valley bottom are due to a lower occult precipitation frequency and point to a contamination of the samples by local pollution sources not captured by the used emission data sources.

Abstract:

In the anthropogenic fire-disturbed ecosystem of the San Francisco Valley in the Andes of southeastern Ecuador, dense stands of an aggressive invasive weed, the southern bracken fern (Pteridium arachnoideum and Pteridium caudatum), dominate the landscape. To secure sustainable land management in the region, a comprehensive understanding of bracken spatial-distribution patterns and life cycle dynamics is crucial. We investigated the possibility of detecting bracken-infested areas and frond status (live, fungi-infected, and dead) by means of a high-resolution QuickBird scene from October 2010 and spectral signatures based on field spectroscopy. After image pre-processing, a two-step classification procedure first delineates the bracken-infested area by means of a maximum-likelihood hard classification. The probability-guided unmixing classifier with field-derived end-members is applied in the second step to obtain the fractional cover of the different frond statuses per pixel. The results showed that the areas infested by bracken could be distinguished from the other land-cover classes with high accuracy (overall accuracy of 0.9973). Also, the three frond statuses could be accurately classified at the sub-pixel level. The ‘dead’ class was the dominant frond status at the time of image acquisition (October 2010). We conclude that the extreme dry spell in October 2010 was particularly responsible for this dominance.